Feasibility and impact of brief interventions for frequent cannabis users in Canada

Journal of Substance Abuse Treatment 44 (2013) 132–138

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Journal of Substance Abuse Treatment

Feasibility and impact of brief interventions for frequent cannabis users in Canada Benedikt Fischer, Ph.D. a, b,⁎, Meghan Dawe, M.A. b, Fraser McGuire, M.A. b, Paul A. Shuper, Ph.D. b, Rielle Capler, M.H.A. a, Dan Bilsker, Ph.D. a, Wayne Jones, MS.c. a, Benjamin Taylor, MS.c. b, c, Katherine Rudzinski, M.A. b, c, Jürgen Rehm, Ph.D. b, c a b c

Centre for Applied Research in Mental Health and Addictions, Faculty of Health Sciences, Simon Fraser University, 2400, 515 West Hastings St., Vancouver, BC, Canada V6B 5K3 Social and Epidemiological Research Department, Centre for Addiction and Mental Health, 33 Russell St., Toronto, Ontario, Canada M6J 1H4 Dalla Lana School of Public Health, University of Toronto, 155 College St., Toronto, Ontario, Canada M5T 3M7

a r t i c l e

i n f o

Article history: Received 14 July 2011 Received in revised form 19 March 2012 Accepted 20 March 2012 Keywords: Cannabis use Frequent use Young adults Brief interventions Prevention

a b s t r a c t Cannabis use is prevalent among young people, and frequent users are at an elevated risk for health problems. Availability and effectiveness of conventional treatment are limited, and brief interventions (BIs) may present viable alternatives. One hundred thirty-four young high-frequency cannabis users from among university students were randomized to either an oral (C-O; n = 25) or a written experimental cannabis BI (C-W; n = 47) intervention group, or to either an oral (H-O; n = 25) or written health BI (H-W; n = 37) control group. Threemonth follow-up assessments based on repeated measures analysis of variance techniques found a decrease in the mean number of cannabis use days in the total sample (p = 0.024), reduced deep inhalation/breathholding use in the C-O group (p = 0.003), reduced driving after cannabis use in the C-W group (p = 0.02), and a significant reduction in deep inhalation/breathholding in the C-O group (p = 0.011) compared with controls. Feasibility and short-term impact of the BIs were demonstrated, yet more research is needed. © 2013 Elsevier Inc. All rights reserved.

1. Introduction Cannabis is the most widely used illicit psychoactive substance in Canada, which features among the highest use levels among Western countries (Room, Fischer, Hall, Lenton, & Reuter, 2010). Recent national general population surveys suggest that at least one in 10 adult Canadians are active cannabis users (Adlaf, Begin, & Sawka, 2005; Health Canada, 2009). Importantly, cannabis use is most prevalent among adolescents and young adults (e.g., ages 16 to 29 years), including post-/secondary student populations (Adlaf, Begin, et al., 2005; Johnston, O'Malley, Bachman, & Schulenberg, 2009; Office of Applied Studies, Substance Abuse and Mental Health Services Administration (SAMHSA), (2009)). In the Canadian general population 26%–37% of respondents, ages 15 to 24 years, reported current cannabis use (Adlaf, Begin, et al., 2005; Health Canada, 2009). In a representative survey of 6,282 undergraduate students from universities across Canada, 32% of respondents reported past year cannabis use (Adlaf, Demers, & Gliksman, 2005). Cannabis use is associated with a variety of potential health risks and harms, including: memory and psychomotor impairment, accidental injury (e.g., related to cannabis impaired driving), mental

⁎ Corresponding author. Centre for Applied Research in Mental Health and Addictions (CARMHA), Faculty of Health Sciences, Simon Fraser University, 2400 515W Hasting St., Vancouver, BC V6B 5K3, Canada. Tel.: + 1 778 782 5148; fax: + 1 778 782 7768. E-mail address: bfi[email protected] (B. Fischer). 0740-5472/$ – see front matter © 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.jsat.2012.03.006

health disorders (e.g., psychotic symptoms), dependence, bronchial or pulmonary illnesses, and other illicit drug use (Fergusson, Horwood, & Swain-Campbell, 2003; Gerberich et al., 2003; Hall, 2009; Hall & Pacula, 2003; Iversen, 2007; Kandel & Chen, 2000; Mehra, Moore, Crothers, Tetrault, & Fiellin, 2006; Moore et al., 2007; Ramaekers, Bergaus, van Laar & Drummer, 2004; Tetrault et al., 2007). However, epidemiological data suggest that it is predominantly intensive or high-frequency users (e.g., daily or near daily use) who are at risk to experience the above-mentioned problem outcomes (Fischer, Rehm, & Hall, 2009; Hall, 2009; Hall & Fischer, 2010; Hall & Pacula, 2003; Room et al., 2010). For example, high-frequency cannabis use has been shown to be associated with involvement in cannabis-related accidents, psychosis, dependence, and bronchial problems, compared with less frequent use patterns (Coffey et al., 2002; Degenhardt et al., 2007; Fergusson & Horwood, 2001; Fergusson et al., 2003; Fischer, Rodopulos, Rehm, & Ivsins, 2006; Mittleman, Lewis, Maclure, Sherwood, & Muller, 2001; Moore et al., 2007; Stefanis et al., 2004; Tashkin, 1999; Taylor et al., 2002). The above epidemiological patterns make young high-frequency cannabis users a primary target for interventions from a public health perspective, especially when considering the potential cannabis use-related burden of disease over the lifespan in this population. Besides general prevention, most current interventions for cannabis use consist of treatment in the form of cognitive– behavioral or psycho-therapeutic treatment (Copeland & Swift, 2009; Nordstrom & Levin, 2007). While the demand for cannabis use treatment in Canada has risen substantially in recent years—

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estimated to constitute 25% to 30% of substance use treatment admissions (Rush & Urbanoski, 2007)—treatment is resourceintensive and unable to address the current extent of problematic cannabis use. Additionally, conventional treatments are limited in their effectiveness and tend to succeed only partially in reducing use or problems, and in most cases with only short-term effects (Copeland & Swift, 2009; McRae, Budney, & Brady, 2003). “Brief Interventions” (BIs) have evolved and proliferated as a key intervention tool in public health approaches to psychoactive substance use in recent years (Babor & Kadden, 2005; Babor et al., 2007; Madras et al., 2009). BIs are short, concise, and easy to administer—commonly consisting of one or two BI units involving information, awareness or motivational components—targeted at predefined risk or target groups, and can be delivered in medical (e.g., GP offices) or more general, non-medical settings (Babor et al., 2007). Large-scale BI studies include the SBIRT (Screening, Brief Interventions, Referral to Treatment) initiative which screened almost 1 million people for alcohol and other drug use, a sizeable proportion of which was referred to BI depending on their risk or problem score (Babor et al., 2007; Madras et al., 2009). BIs have been effective in reducing key substance use risk or harm indicators, including use frequency or quantity, injury, and impaired driving (Madras et al., 2009; Schermer, Moyers, Miller, & Bloomfield, 2006; Whitlock, Polen, Green, Orleans, & Klein, 2004). BIs have recently also been developed and tested specifically for cannabis use, and demonstrated potential for reducing cannabis userelated risk or harm indicators when compared with untreated controls (Benyamina, Lecacheux, Blecha, Reynaud, & Lukasiewcz, 2008; McRae et al., 2003; Nordstrom & Levin, 2007). While not consistently reaching the efficacy levels of extended (e.g., multisession cognitive-behavioral treatment) treatments, BIs are likely to be more cost-effective (Copeland & Swift, 2009; Denis, Lavie, Fatséas & Auriacombe, 2006; McRae, Budney & Brady, 2003). Specifically, a few (UK and Australia) studies have recently tested BIs—involving one or two sessions—for younger cannabis users, and found short-term effectiveness in reducing cannabis use frequency, quantity and problem indicators (Martin & Copeland, 2008; McCambridge, Slym, & Strang, 2008; Walker, Roffman, Stephens, Berghuis, & Wakana, 2006). Notably, BIs for cannabis use have relied on differential intervention design or delivery (e.g., in-person interventions, written materials, multi-media approaches) (Lang, Engelander, & Brooke, 2000; Stephens, Roffman, Fearer, Williams, & Burke, 2007), although little is known about the potentially differential impact of these approaches. As cannabis treatment is limited in availability, BIs—while only marginally used and studied in Canada—may constitute a potentially crucial intervention component in a public health approach to highrisk cannabis use among young people (Copeland & Swift, 2009; Martin & Copeland, 2008; Walker, Roffman, Stephens, Berghuis & Wakana, 2006). This study aimed to recruit a sample of young adult, high-frequency cannabis users from a university student population, and to assess the feasibility and short-term impact of newly developed BIs for cannabis use—utilizing two different delivery modalities—in a controlled design. 2. Methods 2.1. Sample and recruitment Study participants were recruited from two university campuses in Toronto—Canada's largest metropolitan area—by way of massadvertising (i.e., study information posters providing key details and criteria of the study) between October 2009 and March 2010. Study applicants then called a study telephone line, where they were screened for eligibility. Eligibility criteria included: a) 18–28 years of age, b) active full-time university enrolment, c) active cannabis use for

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at least 1 year, and d) for at least 12 of the past 30 days. If eligibility for the study was confirmed, candidates were invited for an in-person study appointment, consisting of a baseline assessment and an intervention. The study received 257 calls, determined 193 callers to be eligible, and conducted a total of 134 valid baseline assessments. The 59 callers deemed eligible for the study but not assessed either did not accept the invitation for study participation under the conditions explained, or did not show up for their in-person appointment (see also Fig. 1). 2.2. Assessments and interventions Baseline assessments were conducted by one of two research assistants, consisting of a relatively short (25–30 minutes) interviewer-administered questionnaire involving mainly closed-ended (dichotomous or scaled) question items focusing on socio-demographic characteristics; cannabis use, risk and outcome; and other drug use and health indicators. The assessment questionnaire items were partly custom-designed by the investigators and partly taken from established instruments (e.g., Cannabis Use Disorders Identification Test) (Piontek, Kraus, & Klempova, 2008). This approach was chosen to ensure that the theoretically driven outcome variables of the study and the co-variates were adequately assessed. A salivary test (Oraline) was administered to confirm recent cannabis use. Immediately following the baseline assessments, participants were randomized (by way of two rounds of flipping of a coin) to one of four BIs: 1a) an orally-delivered cannabis BI (C-O; experimental intervention 1); 1b) a written cannabis BI (C-W; experimental intervention 2); 2a) an orally-delivered BI on general health (H-O; control intervention 1);and 2b) a written BI on general health (H-W; control intervention 2). The experimental interventions consisted of short, fact-based and non-judgmental information on cannabis-related health risks (e.g., “Over the long term, heavy cannabis smoking can lead to problems ranging from shortness of breath to chronic bronchitis”), concrete suggestions and techniques to modify such risks (e.g., “Use less frequently—only use on one or two select days per week [e.g., weekend] on which cannabis interferes least with obligations”), along with some brief motivational components (e.g., developing personalized harm reduction goals; identifying possible barriers to achieving one's goals; creating strategies for overcoming barriers). The C-O version was delivered in person—in an interactive, casual and non-judgmental style—by a psychologist with training in substance use and health behavior counseling in a session 20–30 minutes long on average, whereas the C-W was provided in the form of an eight-page, colorfully designed and laid-out booklet with corresponding written text content. The two control (H-O and H-W) interventions were designed and delivered in the exact same ways, except that they were composed of general health information content (e.g., nutrition, stress, exercise). Following the completion of the baseline assessment and respective BI module, participants were scheduled for their followup assessment, at a date exactly 3 months following the baseline assessment, as well as provided with contact information for followup purposes. In cases where participants did not return for their prescheduled follow-up assessment, study staff re-contacted the participant, and set a new appointment for the follow-up assessment. Participants were included in the follow-up if the follow-up assessment was completed within a maximum of 4 weeks after the originally scheduled date. Follow-up assessments consisted of an abbreviated version of the baseline assessment, and in addition included some select open-ended qualitative question items. The follow-up assessments were administered by the same interviewer who had conducted the baseline assessment; this approach, together with the choice for an interviewer-administered questionnaire, was in part driven by the rationale that a personal rapport between the

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B. Fischer et al. / Journal of Substance Abuse Treatment 44 (2013) 132–138

Fig. 1. Study flow chart.

participant and the interviewer would allow for easy clarification of study procedure or assessment details, as well as increase overall comfort level with the study and thus follow-up retention. Respondents received a $20 honorarium for the baseline assessment, and a $30 honorarium for the follow-up assessment, for their time and effort; differential honoraria amounts were used in order to increase retention for follow-up. The study protected participants’ anonymity; no names or information that would allow identification of the person were collected. Instead, they were assigned a unique and anonymous, but easily memorable study participant code, which was also used to reidentify participants at follow-up and for data linkage. Both baseline and follow-up assessments were conducted in a private room at the investigators’ research laboratory and assured full confidentiality of the data. Prior to the baseline assessment, participants provided informed consent. The study protocol was approved by the investigators’ institution's research ethics board (REB) as well as by the two universities where participants were recruited.

2.3. Measures and analyses Four measures taken at baseline and at the 3-month follow-up were used to investigate pre- and post-intervention outcomes for each of the two intervention (C-O and C-W) and two control groups (H-O and H-W), the combined cannabis intervention groups (C-O + C-W), the combined control groups (H-O + H-W), and the total sample. The measures were chosen as distinctly pertinent yet usermodifiable for acute and/or long-term cannabis use-related harms based on the literature, and had been explicitly addressed in the (experimental) BI modules. These four measures were as follows:

(1) Number of days of cannabis use over the last 30 days (measured as a continuous variable) (2) Average number of distinct episodes of cannabis use on cannabis use days in the last 30 days (continuous) (3) Use of deep inhalation/breathholding techniques when using cannabis in the past 30 days (yes/no) (4) Driving a vehicle within 2 hours of cannabis use in the last 30 day (yes/no). Two different main analyses were executed. First, a standard pre– post analysis was run to compare differences in mean scores at baseline and at 3 months using a repeated measures analysis of variance using the Proc Mixed suite of commands in SAS (Moser, 2004). A total of seven analyses were run for each measure: baseline scores were compared with follow-up separately for the total sample (1), the combined intervention group (1), the combined control group (1), and then for each intervention group (2) and control group (2). Differences in prevalence scores (proportions and therefore categorical) between baseline and follow-up for deep inhalation and driving after cannabis use were compared via McNemar's test (Fleiss, 1981; Twisk, 2003), which takes into account the correlated nature of preand post-measures in clinical trials. The second main analysis was a comparison of intervention versus control groups for each of the measures assessed in the first analysis. For continuous outcomes, Proc Mixed was used again, but this time the group × time interaction component was investigated to assess differences between intervention and control groups between baseline and 3-month follow-up. This differs from the first analysis in that differences over time and between different intervention groups could be assessed. For categorical outcomes, the CATMOD procedure in SAS was used (http://www.ats.ucla.edu/stat/sas/examples/icda/chapter8.htm).

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Table 1 Number of cannabis use days in the last 30 days at baseline and at 3-month follow-up assessments for total sample and different BI groups. Sample

Total sample Cannabis oral + written Health oral + written Cannabis oral Cannabis written Health oral Health written

n

Baseline

Baseline (follow-up)

Mean

95% CI

Mean

95% CI

134 72 62 25 47 25 37

23.79 23.83 23.74 21.96 24.82 21.36 25.35

22.81–24.77 22.54–25.12 22.20–25.28 19.58–24.33 23.32–26.32 18.61–24.11 23.65–27.06

22.41 22.31 22.53 18.78 24.38 21.18 23.55

20.97–23.84 20.26–24.35 20.48–24.58 15.04–22.52 22.11–26.66 17.88–24.48 20.83–26.27

(113) (62) (51) (23) (39) (22) (29)

Feasibility was determined by indications of whether the intervention a) evoked interest from potential participants as determined by calls to the study recruitment line; b) proved able to attract and retain interested people as measured by enrolment; c) proved able to retain participants’ interest and participation during the study as measured by completion of the assessment and intervention, and possible dropouts; and d) proved able to ensure follow-up assessments of participants. All data analyses were computed using SAS software, Version 9.2 of the SAS System for Windows (SAS Institute Inc., 2006).

3. Results Of the baseline sample (N = 134), 113 (84.3%) were retained and assessed for follow-up. The follow-up sample was predominantly male (68.1%), had a mean age of 20.6 years (95% confidence interval [CI]: 20.1–21.0), and a mean number of 2.6 university years (95% CI: 2.3–2.9). The follow-up sample assessed did not differ significantly from those lost to follow-up on these indicators. In terms of ethnic background the majority of the follow-up sample was White/ Caucasian (74%), followed by Middle-Eastern or Arabic (10%), Asian (8%) and others (8%). Ethnicity was not significantly associated with follow-up status at 3 months (chi-square (3) = 6.3; p = 0.098). There were a number of significant changes observed in the study cohort regarding key outcome indicators between the baseline and the 3-month follow-up assessments (see Tables 1–3). The mean number of cannabis use days fell from 23.79 to 22.41 (p = 0.024) in the total sample, but no significant differences were detected within each group over time. No changes in the mean number of cannabis use episodes were observed between baseline and follow-up. The prevalence of deep inhalation/breathholding use fell from 79.65% to 63.72% (p b 0.000) in the total sample, from 77.78% to 51.61% (p = 0.001) in the combined C-O + C-W intervention groups, and from 80.00% to 39.13% (p = 0.003) in the C-O group. The prevalence of driving after cannabis use fell from 37.59% to 29.20% (p = 0.011) in the total sample, from 44.44% to 30.65% (p = 0.020) in the combined Table 2 Prevalence of use of deep inhalation/breathholding in the last 30 days at baseline and at follow-up assessments for total sample and different BI groups. Sample

n

Baseline

Follow-up

p-Value

Baseline Prevalence 95% CI Prevalence 95% CI (follow-up) (%) (%) Total sample 134 (113) Cannabis 72 (62) oral + written Health 62 (51) oral + written Cannabis 25 (23) oral Cannabis 47 (39) written Health oral 25 (22) Health written 37 (29)

79.65 77.78

– –

63.72 51.61

– –

0.000 0.001

80.65

–

78.43

–

0.257

80.00

–

39.13

–

0.003

76.60

–

58.97

–

0.058

76.00 83.78

– –

72.73 82.76

– –

0.157 0.655

Follow-up

p-Value

0.024 0.094 0.133 0.125 0.469 0.737 0.108

C-O + C-W intervention groups, and from 46.81% to 30.77% (p = 0.020) in the C-W group. In the second analysis comparing the intervention and the control groups via the interaction component, the change in deep inhalation/breathholding was found to be significantly different in the combined C-O + C-W groups compared with the combined H-O + H-W groups (p = 0.014), as well as for the C-O versus the H-O group (p = 0.011). Overall, the study protocol and BI modules tested proved to be feasible. The study successfully recruited more than the minimum size of participant sample (n = 100) predetermined prior to study launch. All participants enrolled completed the baseline assessment and the BI module they were assigned to, and the study's retention rate for follow-up was 84%. In addition to these quantitative indicators, the study collected select qualitative feedback data from participants, who mainly indicated they had received interesting and helpful information by ways of the BIs (these data are reported in detail in a separate paper; see Rudzinski et al., 2012). 4. Discussion This study explored the feasibility and short-term impact of custom-designed BI modules with two distinct delivery modes for young high-frequency cannabis users, recruited from university student populations in Toronto, Canada, in a controlled study setting. Overall, our results indicate that BIs for high-frequency cannabis use in the target population are feasible and produce detectable shortterm effects in as far as study recruitment and follow-up were effective. In addition, the intervention components of our study received largely positive—and some constructively critical—qualitative feedback (see Rudzinski et al., 2012) which will be useful for further study and intervention development. In terms of outcome findings, our study echoes results—and further contributes to a limited body of literature—from other studies on various BI tools for problematic cannabis use in young user populations, which also observed short-term reductions in key risk

Table 3 Prevalence of cannabis and driving in the last 30 days at baseline and at follow-up assessments for total sample and different BI groups. Sample

n

Baseline

Follow-up

Baseline Prevalence 95% (follow-up) (%) CI Total sample 133a (113) Cannabis 72 (62) oral + written Health 61a (51) oral + written Cannabis oral 25 (23) Cannabis 47 (39) written Health oral 24a (22) Health written 37 (29)

p-Value

Prevalence 95% (%) CI

37.59 44.44

– –

29.20 30.65

– –

0.011 0.020

29.51

–

27.45

–

0.257

40.00 46.81

– –

30.43 30.77

– –

0.414 0.020

29.17 29.73

– –

27.27 27.59

– –

0.317 0.414

a One subject responded “don't know” to this item at baseline and was not included in the baseline calculations.

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indicators (Copeland, Swift, Roffman, & Stephens, 2001; Dennis et al., 2004; Martin, Copeland, & Swift, 2005; Stephens, Roffman, & Curtis, 2000; White et al., 2006). While none of the changes observed in our study eliminated any of the pre-defined cannabis-related risk behaviors, they resulted in select sizeable reductions similar to effects commonly accomplished by time- and resource-intensive treatment (Denis et al., 2006) which in turn may translate into reduced risk for subsequent problem or disease outcomes in the target population. In addition, our study is the first to test such BI interventions in a Canadian setting, and hence offers important preliminary evidence in this context. Specifically, our study found several significant changes in key BI outcome indicators at the 3-month follow-up. First, the pre–post analysis found a decrease in the number of cannabis use days for the total study sample; this decrease approached but did not reach statistical significance for the combined C-O + C-W intervention groups, which may possibly be related to limited statistical power in this exploratory study design with limited sample sizes. The reduction in cannabis use frequency observed was rather small— and unlikely in itself to result in substantive health risk reductions in the sample given that cannabis use post-intervention still occurred on a daily or near-daily basis for most participants—and notably smaller than effects observed in several other cannabis BI studies. For example, Copeland et al. (2001) single session BI group reported an increase to almost half (45%) of cannabis use abstinence days in the follow-up period. Similarly, Stephens et al. (2000) reported a reduction of two thirds in the number of cannabis use days in their BI group at the 4-month follow-up, and Martin and Copeland (2008) still reported a cannabis use day reduction of almost 20% in the 90day follow-up period. While relatively small, the reduction of cannabis use days observed in our study cannot with certainty be attributed to content-specific interventions effects—as it involved both the intervention and the control groups—yet may also be caused by other extrinsic (e.g., seasonal or life-contextual) effects in the study population. This reflects the nature of findings of other cannabis BI studies. For example, studies by Walker et al. (2006) and Gray, McCambridge, and Strang (2005)—both reporting on BIs aiming at cannabis use among young people, and the former study detecting significant reductions in cannabis use in the overall study sample— found no significant differences in effects between content interventions and controls. These circumstances raise the wider question whether observed effects are primarily a product of information content provided, or mainly a more general “attention” effect to the target population's substance use and behavior (Albarracin et al., 2005; Fishbein & Ajzen, 2005). This issue requires more investigation, in that it also has important implications for the design of future intervention programs. Notably, two of the four pre-identified risk outcome indicators (i.e., deep inhalation/breathholding, cannabis use and driving) indicated significant decreases in the combined intervention groups, and at least one of the two experimental BI groups (i.e., C-O or C-W). Both of these risk outcome indicators featuring positive short-term changes in the experimental BI groups are important factors for cannabis use-related individual and public health, and specifically bronchial or pulmonary problems, and motor vehicle accident (MVA) involvement (Anthony, 2006; Hashibe et al., 2005; Ramaekers, Berghaus, van Laar, & Drummer, 2004; Room et al., 2010). More specifically, the prevalence of risky cannabis inhalation techniques was cut in half in the C-O group, and substantively reduced in the C-W group. Risky cannabis inhalation practices are an important, albeit relatively little examined, topic in the cannabis health risk literature, and may substantially contribute to acute or long-term health problems (Fischer, Hall, Room, Goldner, & Rehm, 2011; Swift, Copeland, & Lenton, 2000). Other cannabis BI studies have not yet focused on these outcomes, yet the fairly sizeable reductions reported

in our study's experimental groups point to the potential of specific health behavior change in this concrete realm. The exact nature of the dynamics and effects—as well as the potential role of social desirability in post-intervention assessments—needs to be further investigated for consideration in future interventions. The changes in outcomes observed regarding cannabis use and driving are encouraging. The combined C-O + C-W intervention groups showed a one-third reduction in prevalence of cannabis and driving, compared with a non-effect in the controls. The observed experimental effect in the intervention groups may translate into tangible and important risk reduction strategies for cannabis userelated MVAs on a population level, given that overall cannabis use impaired driving is associated with substantively increased risk for MVA involvement (Kelly, Darke, & Ross, 2004; Ramaekers, Berghaus, van Laar & Drummer, 2004). Furthermore, cannabis use impaired driving rates among young people in Canada are high, and conventional interventions (e.g., law enforcement) currently seem to have little impact in reducing such risk behavior (Mann et al., 2007; McGuire, Dawe, Shield, Rehm, & Fischer, 2011). Our study's positive results contribute to the wider context of mixed evidence on the effects of BIs on substance use impaired driving. For example, Bernstein et al.'s (2009) study among a sample of young cannabis users who were administered a BI following admission to a pediatric emergency department found no differences between intervention and control groups regarding cannabis use and driving. Conversely, BI measures aimed at alcohol use have demonstrated positive post-intervention effects on alcohol impaired driving convictions among a sample admitted to a hospital after a motor– vehicle collision (Schermer et al., 2006). Our study found positive short-term results for both the written as well as the oral delivery modules of the cannabis BI, albeit for different harm indicators and on potentially different effect scales, indicating that the content of the intervention contributed substantively to observed changes in behavior, and both distinct BI delivery methods utilized brought the potential for positive effects. This finding adds to a literature with mixed evidence with regard to BI delivery modalities. For example, White et al. (2006) found no significant differences in the effects of BIs for substance use by inperson or written feedback methods, whereas Stephens et al. (2007) found superior effects associated with a personal feedback compared with a multi-media (non-personal) based BI program. Other studies have suggested that personal delivery or interaction constitute a key element of effective BI modules in which the actual information content provided—vis-à-vis the interpersonal attention to a behavioral issue—may play only a subordinate or minor role (McCambridge et al., 2008). In this wider context, the role of content and intervention design for BI delivery methods—and especially the importance of inter-personal engagement—in achieving desired BI effects need to be thoroughly examined by quantitative outcomes as well as qualitative inquiries into the distinct impact dynamics of different delivery methods as perceived by the target population. Given the high prevalence of cannabis use among young people in Canada, yet the simultaneous limited availability and effectiveness of conventional treatment for problematic cannabis use, BIs could potentially constitute a key intervention element in a public health strategy for high-risk cannabis use in this target group (Babor et al., 2007; Fischer et al., 2009; Madras et al., 2009; McRae et al., 2003). Among BIs’ potential advantages are that they are easily and universally deliverable to key target populations. For example, the interventions described here could easily be delivered in university or other educational settings and do not require specialist staff, making them potentially cost-effective in comparison with standard treatment measures. Our study results are overall encouraging, and provide empirical grounds and evidence for continued research and systematic investigation into the efficacy of BI modules for high-

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frequency cannabis users which should focus on a number of key questions. First, larger-scale studies—ideally in the form of a strongly powered randomized controlled trial (RCT)—with appropriately large sample sizes—need to confirm the findings of this present exploratory study in a Canadian setting. This is necessary also on a more global level, given that many of the existing cannabis BI studies are based on relatively small study populations (e.g., Martin & Copeland, 2008; Walker et al., 2006). Furthermore, a critical issue for BIs is the assessment of longer-term effects and their sustainability, as few cannabis BI studies have looked beyond short-term impact periods (e.g., several months) (Copeland & Swift, 2009; McRae et al., 2003). To respond to this important intervention facet, longitudinal studies with multiple assessment points are warranted, including an assessment of the potential utility of repeat or “booster” interventions. This study has some important limitations. The study relied on a self-identified sample of users recruited from university student populations, which may not be representative for the overall population of young high-frequency users, and cannot be generalized to other study populations. The study relied on self-reported data for outcome measures, although the validity of such data has been demonstrated to be good (Darke, 1998; Del Boca & Darkes, 2003), and the independent and anonymous design of the study did not offer discernable reason for participants to not self-report accurately. Some behavioral changes observed could be the result of extrinsic factors. Finally, given its exploratory nature, the study relied on a small sample, translating into small group sample sizes with limited power. Further systematic research on cannabis BIs with appropriate designs and sample sizes is thus warranted by the result of this exploratory study, focusing on long-term and both quantitative and qualitative outcomes.

Acknowledgments The authors acknowledge funding support from the Canadian Institutes of Health Research (CIHR), specifically Catalyst Grant #211803, for this study. Dr. Fischer, Dr. Rehm and Dr. Shuper also acknowledge funding support from the Ontario Ministry of Health and Long-Term Care. Dr. Fischer furthermore acknowledges salary support from a CIHR/Public Health Agency of Canada Chair in Applied Public Health.

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